Scalable Multi-threaded Community Detection in Social Networks

Riedy, Jason; Bader, David A.; Meyerhenke, Henning

doi:10.1109/ipdpsw.2012.203

Cited by 49 publications

(40 citation statements)

References 30 publications

(35 reference statements)

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“…The algorithm takes a similar approach to [25] and leverages STINGER's parallel insertion and deletion for in-place operation. All vertices are initialized to their own communities, and all edges are scored.…”

Section: Community Detectionmentioning

confidence: 99%

Detecting insider threats in a real corporate database of computer usage activity

Senator

Goldberg

Memory

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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This paper reports on methods and results of an applied research project by a team consisting of SAIC and four universities to develop, integrate, and evaluate new approaches to detect the weak signals characteristic of insider threats on organizations' information systems. Our system combines structural and semantic information from a real corporate database of monitored activity on their users' computers to detect independently developed red team inserts of malicious insider activities. We have developed and applied multiple algorithms for anomaly detection based on suspected scenarios of malicious insider behavior, indicators of unusual activities, high-dimensional statistical patterns, temporal sequences, and normal graph evolution. Algorithms and representations for dynamic graph processing provide the ability to scale as needed for enterpriselevel deployments on real-time data streams. We have also developed a visual language for specifying combinations of features, baselines, peer groups, time periods, and algorithms to detect anomalies suggestive of instances of insider threat behavior. We defined over 100 data features in seven categories based on approximately 5.5 million actions per day from approximately 5,500 users. We have achieved area under the ROC curve values of up to 0.979 and lift values of 65 on the top 50 user-days identified on two months of real data.

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Section: Community Detectionmentioning

confidence: 99%

Detecting insider threats in a real corporate database of computer usage activity

Senator

Goldberg

Memory

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

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“…Detection in research) that searches a dense groups of nodes and its aim is to analyze network to several linked components (communities) in such a way that nodes in each component have high-density connections, while nodes in different components have the lowest density of the proposed methods in this category algorithm SLPA [9], TopGC [10], SVINET [11], MCD [12], CGGC [13], CONCLUDE [14], DSE [15] and SPICi [16] can be cited.…”

Section: Link Based Clustering (Also Known As Communitymentioning

confidence: 99%

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Tabatabaee

2018

IRATJ

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Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them. Basic methods in the field used the criteria coming from graph theory, but new approaches are trying to take advantage of the realm of traditional data exploration and linked data. One of these new approaches is the approach called conceptual link approach, which was introduced to describe the social networks. In this approach, using the concept of contextual links, knowledge of the social network through a conceptual view of the dramatic structure is summarized as means of social networking. Conceptual perspective provides a summary of existing knowledge on a social network.

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“…We also compare the speedup performance with a recently proposed parallel community detection algorithm [39,52] running on shared-memory systems. Their community detection algorithm achieves 5.1x speedup on 16 cores using Intel Xeon X5570 CPU and 9.4x speedup using 32 cores on XMT2 [52] for soc-LiveJournal1 data.…”

Section: Graph Namementioning

confidence: 99%

“…Their community detection algorithm achieves 5.1x speedup on 16 cores using Intel Xeon X5570 CPU and 9.4x speedup using 32 cores on XMT2 [52] for soc-LiveJournal1 data.…”

Section: Graph Namementioning

confidence: 99%

A scalable community detection algorithm for large graphs using stochastic block models

Peng

Zhang

Wong

et al. 2017

IDA

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Scalable Multi-threaded Community Detection in Social Networks

Cited by 49 publications

References 30 publications

Detecting insider threats in a real corporate database of computer usage activity

Detecting insider threats in a real corporate database of computer usage activity

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

A scalable community detection algorithm for large graphs using stochastic block models

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